steven_li
5ba5c7e4c1
集成新的Beaver后端服务到应用实例中，替换原有的nanobot实现。 主要变更包括： - 在Dockerfile和环境配置中添加Beaver相关路径和配置变量 - 更新工作目录结构从.nanobot到.beaver - 实现Beaver引擎加载器，支持配置文件加载和工具组装 - 添加内置工具如ListDirectoryTool、ReadFileTool、SearchFilesTool - 更新消息处理流程，支持通道适配器和网关模式 - 重构技能系统，支持显式工具提示和嵌入式检索 - 改进错误处理和生命周期管理 此变更使应用实例能够使用统一的Beaver后端进行AI代理运行时管理。
118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
"""Beaver 工具执行器。
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这层专门负责把 provider 返回的 tool call 转成真正的工具执行。
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它不关心 provider 是 OpenAI、Anthropic 还是 Codex，只关心：
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1. 工具叫什么
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2. 参数是什么
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3. registry 能不能找到它
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4. 执行结果怎么标准化
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"""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING, Any
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from beaver.tools.base import ToolContext, ToolResult
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from beaver.tools.registry.tool_registry import ToolRegistry
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if TYPE_CHECKING:
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from beaver.engine.providers.base import ToolCallRequest
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class ToolExecutor:
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"""统一执行单个 tool call。"""
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def __init__(self, registry: ToolRegistry) -> None:
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self.registry = registry
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async def execute(
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self,
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tool_name: str,
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arguments: dict[str, Any] | None,
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*,
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context: ToolContext | None = None,
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) -> ToolResult:
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"""按工具名执行一次调用。"""
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tool = self.registry.get(tool_name)
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if tool is None:
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return ToolResult(
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success=False,
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content=f"Tool {tool_name} is not registered.",
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tool_name=tool_name,
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error="tool_not_found",
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)
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return await tool.invoke(arguments or {}, context or ToolContext())
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async def execute_tool_call(
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self,
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tool_call: ToolCallRequest | dict[str, Any],
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*,
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context: ToolContext | None = None,
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) -> ToolResult:
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"""执行 provider 返回的一次结构化 tool call。
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兼容两种输入：
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- `ToolCallRequest`
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- OpenAI 风格 dict
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"""
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try:
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tool_name, arguments = self._normalize_tool_call(tool_call)
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except Exception as exc:
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return ToolResult(
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success=False,
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content=f"Tool call could not be parsed: {exc}",
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tool_name=self._extract_tool_name(tool_call),
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error="tool_call_parse_error",
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)
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parse_error = arguments.pop("__beaver_tool_argument_parse_error__", None)
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if parse_error is not None:
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return ToolResult(
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success=False,
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content=f"Tool call arguments for {tool_name} could not be parsed: {parse_error}",
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tool_name=tool_name,
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error="tool_call_argument_parse_error",
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raw_output=arguments.get("__raw_arguments__"),
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)
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return await self.execute(tool_name, arguments, context=context)
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@staticmethod
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def _normalize_tool_call(tool_call: ToolCallRequest | dict[str, Any]) -> tuple[str, dict[str, Any]]:
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if not isinstance(tool_call, dict):
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name = getattr(tool_call, "name", None)
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arguments = getattr(tool_call, "arguments", {})
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else:
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function = tool_call.get("function")
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if isinstance(function, dict):
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name = function.get("name")
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arguments = function.get("arguments", {})
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else:
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name = tool_call.get("name")
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arguments = tool_call.get("arguments", {})
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if not name:
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raise ValueError("Tool call is missing a tool name")
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if isinstance(arguments, str):
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try:
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arguments = json.loads(arguments)
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except json.JSONDecodeError as exc:
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raise ValueError(f"Tool call arguments for {name!r} are not valid JSON") from exc
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if not isinstance(arguments, dict):
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raise ValueError(f"Tool call arguments for {name!r} must be a dict")
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return str(name), arguments
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@staticmethod
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def _extract_tool_name(tool_call: ToolCallRequest | dict[str, Any]) -> str:
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if not isinstance(tool_call, dict):
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return str(getattr(tool_call, "name", None) or "unknown")
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function = tool_call.get("function")
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if isinstance(function, dict) and function.get("name"):
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return str(function["name"])
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if tool_call.get("name"):
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return str(tool_call["name"])
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return "unknown"
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